Review of AI/ML in Software Defined Network from Past to Present

Software-Defined Networks (SDN) technology disrupts the traditional network architecture's tight link between the data plane and the control plane, enabling network resource economy, security, and controllability. In this study, we carried out a systematic analysis with a specific focus on the application of AI/ML algorithms to enhance SDN functions. Artificial intelligence (AI) or Machine learning (ML) will have significant potential in fields such as route planning, network resource management, traffic scheduling, network security and fault detection, when paired with SDN architecture. Networks have become more complicated and challenging to configure, manage, and monitor as a result of these demands. Researchers and operators recommended using software tools that can monitor and configure networks ondemand to make networks more manageable and controllable. From the perspective of ML algorithms, this study focuses on the applications of traditional AI/ML algorithms in SDNbased networks. Finally, a discussion and analysis of the potential future development of SDN concepts in ML algorithms is addressed. We present a summary of the state-of-the-art after reviewing 1450 publications. Researchers from various domains will find this study useful and essential in fully understanding the fundamental concerns